A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins

Authors

Jianwei Zheng, Chapman UniversityFollow
Guohua Fu, Zhejiang University
Daniele Struppa, Chapman UniversityFollow
Islam Abudayyeh, Loma Linda University
Tahmeed Contractor, Loma Linda University
Kyle Anderson, Chapman University
Huimin Chu, Zhejiang University
Cyril Rakovski, Chapman UniversityFollow

Document Type

Article

Publication Date

3-11-2022

Abstract

Background: Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method: A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four classification schemes responding to different hierarchical levels of the possible IVA origins. For every classification scheme, we compared 98 distinct machine learning models with optimized hyperparameter values obtained through extensive grid search and reported an optimal algorithm with the highest accuracy scores attained on the testing cohorts.

Results: For classification scheme 4, our pioneering study designs and implements a machine learning-based ECG algorithm to predict 21 possible sites of IVA origin with an accuracy of 98.24% on a testing cohort. The accuracy and F1-score for the left three schemes surpassed 99%.

Conclusion: In this work, we developed an algorithm that precisely predicts the correct origins of IVA (out of 21 possible sites) and outperforms the accuracy of all prior studies and human experts.

Comments

This article was originally published in Frontiers in Cardiovascular Medicine, volume 9, in 2022. https://doi.org/10.3389/fcvm.2022.809027

Recommended Citation

Zheng J, Fu G, Struppa D, Abudayyeh I, Contractor T, Anderson K, Chu H and Rakovski C (2022) A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins. Front. Cardiovasc. Med. 9:809027. https://doi.org/10.3389/fcvm.2022.809027

Peer Reviewed

1

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The authors

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This work is licensed under a Creative Commons Attribution 4.0 License.